Biopsy section array of 16 samples used for validation.

<p>Top panel: (i) H&E stained image of sections (scale bar represents 500μm); Asterisk marked samples showed no rejection in pathologist review. (ii) absorbance at 1236 cm^-1 demonstrating differences between samples and different cell types; (iii) Classified IR image showing color coded pixels indicating different pathological classes; Bottom panel: Magnified view of one sample from validation set with matched lower spatial resolution IR image. (iv) H&E stained image of section; (v) Classified 6.25 μm x 6.25 μm pixel size IR image; (vi) Classified 25 μm x 25 μm pixel image.</p

List of metric definitions found useful to differentiate classes- peak height ratio; all values are in wavenumber (cm^-1).

<p>List of metric definitions found useful to differentiate classes- peak height ratio; all values are in wavenumber (cm^-1).</p

Baseline corrected absorption spectra, normalized using the Amide I peak, for all five classes of cells observed in the study.

<p>Important spectral differences observed over the fingerprint spectral region (1500–900 cm^-1) are highlighted in grey and zoomed in without offset.</p

Receiver operating characteristic (ROC) curves demonstrating the accuracy of the classification algorithm (i) Training set at 6.25 μm x 6.25 μm pixel size; (ii) Validation set at 6.25 μm x 6.25 μm pixel size; (iii) Validation set at 25 μm x 25 μm pixel size.

<p>Receiver operating characteristic (ROC) curves demonstrating the accuracy of the classification algorithm (i) Training set at 6.25 μm x 6.25 μm pixel size; (ii) Validation set at 6.25 μm x 6.25 μm pixel size; (iii) Validation set at 25 μm x 25 μm pixel size.</p

Relative intensities of peak height ratios useful in discriminating classes; examples from metric definitions (i) 1239cm^-1 to 1652cm^-1; (ii) 1204cm^-1 to 1236cm^-1; (iii) 1027cm^-1 to 1543cm^-1.

<p>Relative intensities of peak height ratios useful in discriminating classes; examples from metric definitions (i) 1239cm^-1 to 1652cm^-1; (ii) 1204cm^-1 to 1236cm^-1; (iii) 1027cm^-1 to 1543cm^-1.</p

List of metric definitions found useful to differentiate classes- peak area to height ratio and center of gravity; all values are in wavenumber (cm^-1).

<p>List of metric definitions found useful to differentiate classes- peak area to height ratio and center of gravity; all values are in wavenumber (cm^-1).</p

Probability of detection at 10% probability of false alarm.

<p>Probability of detection at 10% probability of false alarm.</p

Towards Translation of Discrete Frequency Infrared Spectroscopic Imaging for Digital Histopathology of Clinical Biopsy Samples

Fourier transform infrared (FT-IR) spectroscopic imaging has been widely tested as a tool for stainless digital histology of biomedical specimens, including for the identification of infiltration and fibrosis in endomyocardial biopsy samples to assess transplant rejection. A major barrier in clinical translation has been the slow speed of imaging. To address this need, we tested and report here the viability of using high speed discrete frequency infrared (DFIR) imaging to obtain stain-free biochemical imaging in cardiovascular samples collected from patients. Images obtained by this method were classified with high accuracy by a Bayesian classification algorithm trained on FT-IR imaging data as well as on DFIR data. A single spectral feature correlated with instances of fibrosis, as identified by the pathologist, highlights the advantage of the DFIR imaging approach for rapid detection. The speed of digital pathologic recognition was at least 16 times faster than the fastest FT-IR imaging instrument. These results indicate that a fast, on-site identification of fibrosis using IR imaging has potential for real time assistance during surgeries. Further, the work describes development and applications of supervised classifiers on DFIR imaging data, comparing classifiers developed on FT-IR and DFIR imaging modalities and identifying specific spectral features for accurate identification of fibrosis. This addresses a topic of much debate on the use of training data and cross-modality validity of IR measurements. Together, the work is a step toward addressing a clinical diagnostic need at acquisition time scales that make IR imaging technology practical for medical use